1. Field of Invention
The present invention relates to a method and apparatus and product for computer-aided diagnosis of cancer, and more particularly, to the diagnosis of breast cancer.
2. Prior Art
The use of diffusible tracers to probe the physiology of tissue has had a long history [see, for example, (1-3)]. With the advent of MRI (Magnetic Resonance Imaging) and the use of low molecular weight lanthanide (such as gadolinium) based contrast agents there has been a great deal of interest in developing novel approaches for the acquisition and analysis of dynamic contrast enhanced MRI (DCE-MRI) data. DCE-MRI involves the acquisition of serial MR images prior to, during, and after the intravenous administration of a contrast agent (4, 5). The acquisition sequences are chosen to be sensitive to the effects of the contrast agent on the nuclear relaxation rates of the water protons in the tissue. Thus, the temporal changes in the signal intensity seen in the dynamic magnetic resonance images constitute a reflection of the uptake of the contrast agent from the blood vessels into the tumor tissue and its washout from the tissue back to the blood vessels. DCE-MRI has gained wide use in diagnostic practice because these uptake and washout processes, as well as the ability of the accumulated contrast agent to reveal or highlight the tumor morphological features, can aid in their characterization [for examples, see (6-12)]. For these reasons, DCE-MRI has become a standard of care for imaging human breast cancer (6-8, 13) and has also come to be used for imaging tumors in other organs; e.g., prostate (10-12, 14), and lung (9). From the diagnostic point of view, there are cumulative evidences that high spatial resolution is crucial (13, 15-17). Moreover, as DCE-MRI is based on the belief that by its very nature it should be sensitive to parameters such as tumor vascularity, vascular permeability, and the cellular density of the tumor (5, 20), it has been proposed as a surrogate imaging biomarker for use in assessing response to therapy, including anti-angiogenic therapy (5, 12, 13, 15-22).
In spite of the widespread use of DCE-MRI, and its usefulness for the aforementioned purposes, there are still ongoing debates about the protocols used to acquire such images, their subsequent analysis, and the accurate presentation of information derived from these studies. In general, the analysis of DCE-MRI data falls into one of three main categories: fitting the data to a pharmacokinetic model [usually a two-compartment model with an idealized arterial input function (23-27)]; semi-quantitative approaches [see, for example, (28, 29)]; and model free approaches such as independent component analysis (ICA)[e.g., (30)] or principal component analysis (PCA) [e.g., (31)].
Methods and apparatus for dynamic contrast-enhanced magnetic resonance imaging of the breast are known. Previous studies have shown that breast malignancies are associated with a pattern of rapid signal enhancement and early washout of the contrast agent that differs from the slower and persistent enhancement pattern seen in normal tissue and in benign breast tumors, suggesting increased angiogenic activity in carcinomas (10, 11).
To date, the analysis of DCE-MRI images of the breast, and other organs has been based in most clinical settings on two approaches: estimation of empirical parameters and parametric analysis based on fitting the data to a pharmacokinetic model, or on identifying patterns based on simulating a pharmacokinetic model and designing a model based protocol such as the Three Time Point method protocol by Degani et al (16,17). On one hand the empiric approaches offer simplicity, however, the resulting parameters may be highly dependent on the specific MRI acquisition protocol employed. Therefore, it may be difficult to accurately correlate the parameters with underlying tumor physiology, and to compare the values of these parameters across different imaging sites. On the other hand, although the pharmacokinetic modeling can yield standardized, physiologically relevant parameters fitting of the dynamic curves on a pixel-by-pixel basis may suffer from the fact that the images are rather noisy; therefore, the fitting algorithms may yield either imprecise or inaccurate estimates of these parameters. In addition, in most cases, an idealized arterial input function is assumed; however, this assumption may not be correct. Alternatively, one may choose to determine an arterial input function In clinical practice, however, this may not be routinely feasible. Furthermore, the two-compartment model does not take into account a number of other factors which could affect the accuracy of the analysis, including the presence of pressure gradients and interstitial diffusion, both of which may alter the dynamic uptake and wash-out patterns. Nevertheless, these approaches have shown fairly good sensitivity and specificity for detecting breast lesions and differentiating benign from malignant breast tumors (7, 17).
Breast cancer is the most common malignancy among women and a major health burden worldwide. The mortality rate from breast cancer has been fairly constant in western countries, and since 1990 a decrease has been detected where screening has been introduced (63-65). One of the indirect beneficial effects of screening might have been a shift towards earlier diagnosis of breast cancer, as a result of the publicity surrounding the disease and its prevention (66). Currently, X-ray and ultrasound mammography are the leading methods used for screening the female population and detect breast cancer. However, breast magnetic resonance imaging (MRI), initiated in the 1980s (67) and particularly, contrast enhanced MRI using Gd-based contrast agents, and demonstrated capability to delineate breast lesions (68). Thus, contrast enhanced breast MRI emerged to become an important adjunct tool for detecting and diagnosing breast lesions, as well as monitoring response to breast cancer treatment (69, 70).
Overall, contrast enhanced MRI exhibits very high sensitivity but variable specificity in discriminating benign from malignant breast diseases, particularly due to the lack of standardization. Currently, a wide range of sequences and protocols, image processing methods, and interpretation criteria are being used. The heterogeneity of breast lesions, particularly of the malignant ones, requires imaging at high spatial resolution (71, 72), yet, obtaining accurate kinetic data requires high temporal resolution (73). Currently, however, it is not possible to achieve simultaneously both high spatial and temporal resolution with a practical signal to noise ratio. Most clinical analyses are based on empirical observations and criteria which depend on readers' experience (74-76). Nevertheless, attempts have been made to better understand the origin of the contrast observed in breast lesions using physiological models that take into account the vascular and tissue-specific features that influence tracer perfusion (77). These model based studies usually yield parametric images that quantitatively map the properties of the microvascular network.